The past decade has witnessed a phenomenal rise in nanotechnology research due to its broad range of applications in diverse fields including food safety, transportation, sustainable energy, environmental science, catalysis, and medicine. The distinctive properties of nanomaterials (nano-sized particles in the range of 1 to 100 nm) make them uniquely suitable for such wide range of functions. The nanoparticles when manufactured using green synthesis methods are especially desirable being devoid of harsh operating conditions (high temperature and pressure), hazardous chemicals, or addition of external stabilizing or capping agents. Numerous plants and microorganisms are being experimented upon for an eco–friendly, cost–effective, and biologically safe process optimization. This review provides a comprehensive overview on the green synthesis of metallic NPs using plants and microorganisms, factors affecting the synthesis, and characterization of synthesized NPs. The potential applications of metal NPs in various sectors have also been highlighted along with the major challenges involved with respect to toxicity and translational research.
A combination of thiol-yne chemistry and esterification reactions were successfully applied for the preparation of dendrimers with an array of terminal functionalities via the divergent growth strategy; maximizing the number of reactive chain ends whilst minimizing the number of reaction steps required in the process.
Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24-90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.
Redox-responsive hyperbranched poly(amido amine)s (PAAs) with tertiary amino cores and amine, poly(ethylene glycol) (PEG) and hydroxyl terminal groups were prepared for DNA delivery respectively. The DNA condensation capability of PAAs was investigated using gel electrophoresis, and the results showed that PAA terminated with 1-(2-aminoethyl)piperazine (AEPZ) (BAA) is the most efficient in binding plasmid DNA (pDNA). The diameter and zeta-potential of polyplexes from PAAs were characterized using dynamic light scattering (DLS), and the morphology of the polyplexes was obtained using atomic force microscopy (AFM). All the PAAs were able to condense pDNA into nanoparticles with diameters between 50 and 200 nm with a positive surface charge when the weight ratio of polymer/DNA was higher than 20. Glutathione (GSH)-induced DNA release from polyplexes and the buffering capability of PAAs were investigated as well. Cytotoxicity of PAAs and in vitro gene transfection of polyplexes were evaluated in HEK293, COS-7, MCF-7 and Hep G2 cell lines, respectively. The results reflect that PAAs show remarkably low or even no cytotoxicity, and that PAA with amino terminal groups mediates the most efficient gene transfection with the transfection efficiency comparable to that of 25 kDa polyethylenimine. Further the effects of the presence of buthionine sulfoximine (BSO) on the transfection efficiency and cytotoxicity of BAA polyplexes were investigated.
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